A control system was designed using adaptive neuro-fuzzy inference system (ANFIS) for industrial-scale batch drying of baker's yeast. The temperature and flow rate of inlet air were considered as the manipulated variables to control the temperature and dry matter of the product, respectively, resulting in two adaptive fuzzy controllers. The membership functions for all inputs were adjusted by a hybrid learning algorithm. The database used in this work comprises large quantities of industrial-scale data (about 570 batches) obtained under different working conditions over one year. This database was used for learning and testing phases of the ANFIS controller. The performance of the proposed controller demonstrates the effectiveness and potential of the proposed ANFIS-based controller.